Overview

Brought to you by YData

Dataset statistics

Number of variables11
Number of observations6362620
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory461.2 MiB
Average record size in memory76.0 B

Variable types

Numeric8
Categorical3

Alerts

amount is highly overall correlated with nameDest and 2 other fieldsHigh correlation
nameDest is highly overall correlated with amount and 2 other fieldsHigh correlation
newbalanceDest is highly overall correlated with amount and 2 other fieldsHigh correlation
newbalanceOrig is highly overall correlated with oldbalanceOrgHigh correlation
oldbalanceDest is highly overall correlated with amount and 2 other fieldsHigh correlation
oldbalanceOrg is highly overall correlated with newbalanceOrigHigh correlation
isFraud is highly imbalanced (98.6%) Imbalance
isFlaggedFraud is highly imbalanced (> 99.9%) Imbalance
amount is highly skewed (γ1 = 30.99394948) Skewed
nameOrig is uniformly distributed Uniform
oldbalanceOrg has 2102449 (33.0%) zeros Zeros
newbalanceOrig has 3609566 (56.7%) zeros Zeros
oldbalanceDest has 2704388 (42.5%) zeros Zeros
newbalanceDest has 2439433 (38.3%) zeros Zeros

Reproduction

Analysis started2025-04-04 10:08:04.196709
Analysis finished2025-04-04 10:12:59.545540
Duration4 minutes and 55.35 seconds
Software versionydata-profiling vv4.13.0
Download configurationconfig.json

Variables

step
Real number (ℝ)

Distinct743
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean243.39725
Minimum1
Maximum743
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size48.5 MiB
2025-04-04T15:42:59.696515image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile16
Q1156
median239
Q3335
95-th percentile490
Maximum743
Range742
Interquartile range (IQR)179

Descriptive statistics

Standard deviation142.33197
Coefficient of variation (CV)0.58477232
Kurtosis0.32907056
Mean243.39725
Median Absolute Deviation (MAD)92
Skewness0.37517689
Sum1.5486442 × 109
Variance20258.39
MonotonicityIncreasing
2025-04-04T15:43:00.101704image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19 51352
 
0.8%
18 49579
 
0.8%
187 49083
 
0.8%
235 47491
 
0.7%
307 46968
 
0.7%
163 46352
 
0.7%
139 46054
 
0.7%
403 45155
 
0.7%
43 45060
 
0.7%
355 44787
 
0.7%
Other values (733) 5890739
92.6%
ValueCountFrequency (%)
1 2708
 
< 0.1%
2 1014
 
< 0.1%
3 552
 
< 0.1%
4 565
 
< 0.1%
5 665
 
< 0.1%
6 1660
 
< 0.1%
7 6837
 
0.1%
8 21097
0.3%
9 37628
0.6%
10 35991
0.6%
ValueCountFrequency (%)
743 8
 
< 0.1%
742 14
< 0.1%
741 22
< 0.1%
740 6
 
< 0.1%
739 10
< 0.1%
738 10
< 0.1%
737 10
< 0.1%
736 14
< 0.1%
735 12
< 0.1%
734 8
 
< 0.1%

type
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size48.5 MiB
1
2237500 
3
2151495 
0
1399284 
4
532909 
2
 
41432

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters6362620
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row4
4th row1
5th row3

Common Values

ValueCountFrequency (%)
1 2237500
35.2%
3 2151495
33.8%
0 1399284
22.0%
4 532909
 
8.4%
2 41432
 
0.7%

Length

2025-04-04T15:43:00.274927image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-04T15:43:00.409326image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 2237500
35.2%
3 2151495
33.8%
0 1399284
22.0%
4 532909
 
8.4%
2 41432
 
0.7%

Most occurring characters

ValueCountFrequency (%)
1 2237500
35.2%
3 2151495
33.8%
0 1399284
22.0%
4 532909
 
8.4%
2 41432
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6362620
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 2237500
35.2%
3 2151495
33.8%
0 1399284
22.0%
4 532909
 
8.4%
2 41432
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6362620
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 2237500
35.2%
3 2151495
33.8%
0 1399284
22.0%
4 532909
 
8.4%
2 41432
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6362620
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 2237500
35.2%
3 2151495
33.8%
0 1399284
22.0%
4 532909
 
8.4%
2 41432
 
0.7%

amount
Real number (ℝ)

High correlation  Skewed 

Distinct5316900
Distinct (%)83.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean179861.9
Minimum0
Maximum92445517
Zeros16
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size48.5 MiB
2025-04-04T15:43:00.600755image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2224.0995
Q113389.57
median74871.94
Q3208721.48
95-th percentile518634.2
Maximum92445517
Range92445517
Interquartile range (IQR)195331.91

Descriptive statistics

Standard deviation603858.23
Coefficient of variation (CV)3.3573437
Kurtosis1797.9567
Mean179861.9
Median Absolute Deviation (MAD)68393.655
Skewness30.993949
Sum1.1443929 × 1012
Variance3.6464476 × 1011
MonotonicityNot monotonic
2025-04-04T15:43:00.788547image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000000 3207
 
0.1%
10000 88
 
< 0.1%
5000 79
 
< 0.1%
15000 68
 
< 0.1%
500 65
 
< 0.1%
100000 42
 
< 0.1%
21500 37
 
< 0.1%
120000 29
 
< 0.1%
135000 20
 
< 0.1%
0 16
 
< 0.1%
Other values (5316890) 6358969
99.9%
ValueCountFrequency (%)
0 16
< 0.1%
0.01 1
 
< 0.1%
0.02 3
 
< 0.1%
0.03 2
 
< 0.1%
0.04 1
 
< 0.1%
0.06 1
 
< 0.1%
0.07 1
 
< 0.1%
0.09 1
 
< 0.1%
0.1 1
 
< 0.1%
0.11 2
 
< 0.1%
ValueCountFrequency (%)
92445516.64 1
< 0.1%
73823490.36 1
< 0.1%
71172480.42 1
< 0.1%
69886731.3 1
< 0.1%
69337316.27 1
< 0.1%
67500761.29 1
< 0.1%
66761272.21 1
< 0.1%
64234448.19 1
< 0.1%
63847992.58 1
< 0.1%
63294839.63 1
< 0.1%

nameOrig
Real number (ℝ)

Uniform 

Distinct6353307
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3176678.1
Minimum0
Maximum6353306
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size24.3 MiB
2025-04-04T15:43:00.984278image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile317651.95
Q11588331.8
median3176672.5
Q34765048.2
95-th percentile6035656
Maximum6353306
Range6353306
Interquartile range (IQR)3176716.5

Descriptive statistics

Standard deviation1834064.2
Coefficient of variation (CV)0.57735285
Kurtosis-1.200028
Mean3176678.1
Median Absolute Deviation (MAD)1588358.5
Skewness-1.6452702 × 10-5
Sum2.0211996 × 1013
Variance3.3637914 × 1012
MonotonicityNot monotonic
2025-04-04T15:43:01.172317image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2964663 3
 
< 0.1%
4260723 3
 
< 0.1%
4857429 3
 
< 0.1%
5447168 3
 
< 0.1%
2575378 3
 
< 0.1%
2226120 3
 
< 0.1%
1520460 3
 
< 0.1%
3284654 3
 
< 0.1%
3609974 3
 
< 0.1%
4380559 3
 
< 0.1%
Other values (6353297) 6362590
> 99.9%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
6353306 1
< 0.1%
6353305 1
< 0.1%
6353304 1
< 0.1%
6353303 1
< 0.1%
6353302 1
< 0.1%
6353301 1
< 0.1%
6353300 1
< 0.1%
6353299 1
< 0.1%
6353298 1
< 0.1%
6353297 1
< 0.1%

oldbalanceOrg
Real number (ℝ)

High correlation  Zeros 

Distinct1845844
Distinct (%)29.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean833883.1
Minimum0
Maximum59585040
Zeros2102449
Zeros (%)33.0%
Negative0
Negative (%)0.0%
Memory size48.5 MiB
2025-04-04T15:43:01.370540image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median14208
Q3107315.18
95-th percentile5823702.3
Maximum59585040
Range59585040
Interquartile range (IQR)107315.18

Descriptive statistics

Standard deviation2888242.7
Coefficient of variation (CV)3.4636062
Kurtosis32.964879
Mean833883.1
Median Absolute Deviation (MAD)14208
Skewness5.2491364
Sum5.3056813 × 1012
Variance8.3419457 × 1012
MonotonicityNot monotonic
2025-04-04T15:43:01.557986image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2102449
33.0%
184 918
 
< 0.1%
133 914
 
< 0.1%
195 912
 
< 0.1%
164 909
 
< 0.1%
181 908
 
< 0.1%
109 908
 
< 0.1%
157 902
 
< 0.1%
146 899
 
< 0.1%
128 898
 
< 0.1%
Other values (1845834) 4252003
66.8%
ValueCountFrequency (%)
0 2102449
33.0%
0.05 1
 
< 0.1%
0.18 1
 
< 0.1%
0.21 1
 
< 0.1%
0.44 1
 
< 0.1%
0.67 1
 
< 0.1%
1 370
 
< 0.1%
1.02 1
 
< 0.1%
1.37 1
 
< 0.1%
1.38 1
 
< 0.1%
ValueCountFrequency (%)
59585040.37 1
< 0.1%
57316255.05 1
< 0.1%
50399045.08 1
< 0.1%
49585040.37 1
< 0.1%
47316255.05 1
< 0.1%
45674547.89 1
< 0.1%
44892193.09 1
< 0.1%
43818855.3 1
< 0.1%
43686616.33 1
< 0.1%
42542664.27 1
< 0.1%

newbalanceOrig
Real number (ℝ)

High correlation  Zeros 

Distinct2682586
Distinct (%)42.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean855113.67
Minimum0
Maximum49585040
Zeros3609566
Zeros (%)56.7%
Negative0
Negative (%)0.0%
Memory size48.5 MiB
2025-04-04T15:43:01.750081image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3144258.41
95-th percentile5980262.3
Maximum49585040
Range49585040
Interquartile range (IQR)144258.41

Descriptive statistics

Standard deviation2924048.5
Coefficient of variation (CV)3.4194852
Kurtosis32.066985
Mean855113.67
Median Absolute Deviation (MAD)0
Skewness5.176884
Sum5.4407633 × 1012
Variance8.5500596 × 1012
MonotonicityNot monotonic
2025-04-04T15:43:01.936264image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3609566
56.7%
5888.64 4
 
< 0.1%
15073.44 4
 
< 0.1%
5122 4
 
< 0.1%
36875.73 4
 
< 0.1%
10528.49 4
 
< 0.1%
904.13 4
 
< 0.1%
18392.51 4
 
< 0.1%
32926.52 4
 
< 0.1%
4277.69 4
 
< 0.1%
Other values (2682576) 2753018
43.3%
ValueCountFrequency (%)
0 3609566
56.7%
0.01 1
 
< 0.1%
0.03 1
 
< 0.1%
0.05 1
 
< 0.1%
0.12 1
 
< 0.1%
0.13 1
 
< 0.1%
0.18 1
 
< 0.1%
0.21 1
 
< 0.1%
0.23 1
 
< 0.1%
0.3 1
 
< 0.1%
ValueCountFrequency (%)
49585040.37 1
< 0.1%
47316255.05 1
< 0.1%
43686616.33 1
< 0.1%
43673802.21 1
< 0.1%
41690842.64 1
< 0.1%
41432359.46 1
< 0.1%
40399045.08 1
< 0.1%
39585040.37 1
< 0.1%
38946233.02 1
< 0.1%
38939424.03 1
< 0.1%

nameDest
Real number (ℝ)

High correlation 

Distinct2722362
Distinct (%)42.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean746427.04
Minimum0
Maximum2722361
Zeros6
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size24.3 MiB
2025-04-04T15:43:02.130550image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile43209.95
Q1216895
median432289
Q31132509.2
95-th percentile2404402
Maximum2722361
Range2722361
Interquartile range (IQR)915614.25

Descriptive statistics

Standard deviation750245.52
Coefficient of variation (CV)1.0051157
Kurtosis0.15893822
Mean746427.04
Median Absolute Deviation (MAD)273664
Skewness1.207225
Sum4.7492316 × 1012
Variance5.6286834 × 1011
MonotonicityNot monotonic
2025-04-04T15:43:02.325928image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
84652 113
 
< 0.1%
567820 109
 
< 0.1%
472721 105
 
< 0.1%
320660 102
 
< 0.1%
349730 101
 
< 0.1%
174831 101
 
< 0.1%
409775 99
 
< 0.1%
233498 99
 
< 0.1%
106929 98
 
< 0.1%
6969 97
 
< 0.1%
Other values (2722352) 6361596
> 99.9%
ValueCountFrequency (%)
0 6
 
< 0.1%
1 13
< 0.1%
2 13
< 0.1%
3 9
< 0.1%
4 16
< 0.1%
5 16
< 0.1%
6 4
 
< 0.1%
7 16
< 0.1%
8 1
 
< 0.1%
9 16
< 0.1%
ValueCountFrequency (%)
2722361 1
< 0.1%
2722360 1
< 0.1%
2722359 1
< 0.1%
2722358 1
< 0.1%
2722357 1
< 0.1%
2722356 1
< 0.1%
2722355 1
< 0.1%
2722354 1
< 0.1%
2722353 1
< 0.1%
2722352 1
< 0.1%

oldbalanceDest
Real number (ℝ)

High correlation  Zeros 

Distinct3614697
Distinct (%)56.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1100701.7
Minimum0
Maximum3.5601589 × 108
Zeros2704388
Zeros (%)42.5%
Negative0
Negative (%)0.0%
Memory size48.5 MiB
2025-04-04T15:43:02.518214image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median132705.66
Q3943036.71
95-th percentile5147229.7
Maximum3.5601589 × 108
Range3.5601589 × 108
Interquartile range (IQR)943036.71

Descriptive statistics

Standard deviation3399180.1
Coefficient of variation (CV)3.0881938
Kurtosis948.67413
Mean1100701.7
Median Absolute Deviation (MAD)132705.66
Skewness19.921758
Sum7.0033464 × 1012
Variance1.1554425 × 1013
MonotonicityNot monotonic
2025-04-04T15:43:02.705787image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2704388
42.5%
10000000 615
 
< 0.1%
20000000 219
 
< 0.1%
30000000 86
 
< 0.1%
40000000 31
 
< 0.1%
102 21
 
< 0.1%
198 19
 
< 0.1%
125 18
 
< 0.1%
160 18
 
< 0.1%
132 18
 
< 0.1%
Other values (3614687) 3657187
57.5%
ValueCountFrequency (%)
0 2704388
42.5%
0.01 1
 
< 0.1%
0.03 1
 
< 0.1%
0.13 1
 
< 0.1%
0.33 1
 
< 0.1%
0.37 1
 
< 0.1%
0.79 1
 
< 0.1%
1 7
 
< 0.1%
1.39 1
 
< 0.1%
1.64 1
 
< 0.1%
ValueCountFrequency (%)
356015889.4 1
< 0.1%
355553416.3 1
< 0.1%
355381433.6 1
< 0.1%
355380483.5 1
< 0.1%
355185537.1 1
< 0.1%
328194464.9 1
< 0.1%
327998074.2 1
< 0.1%
327963024 1
< 0.1%
327852121.4 1
< 0.1%
327827763.4 1
< 0.1%

newbalanceDest
Real number (ℝ)

High correlation  Zeros 

Distinct3555499
Distinct (%)55.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1224996.4
Minimum0
Maximum3.5617928 × 108
Zeros2439433
Zeros (%)38.3%
Negative0
Negative (%)0.0%
Memory size48.5 MiB
2025-04-04T15:43:02.906508image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median214661.44
Q31111909.2
95-th percentile5515715.9
Maximum3.5617928 × 108
Range3.5617928 × 108
Interquartile range (IQR)1111909.2

Descriptive statistics

Standard deviation3674128.9
Coefficient of variation (CV)2.9992978
Kurtosis862.15651
Mean1224996.4
Median Absolute Deviation (MAD)214661.44
Skewness19.352302
Sum7.7941866 × 1012
Variance1.3499223 × 1013
MonotonicityNot monotonic
2025-04-04T15:43:03.099849image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2439433
38.3%
10000000 53
 
< 0.1%
971418.91 32
 
< 0.1%
19169204.93 29
 
< 0.1%
1254956.07 25
 
< 0.1%
16532032.16 25
 
< 0.1%
1412484.09 22
 
< 0.1%
4743010.67 21
 
< 0.1%
1178808.14 21
 
< 0.1%
7364724.84 21
 
< 0.1%
Other values (3555489) 3922938
61.7%
ValueCountFrequency (%)
0 2439433
38.3%
0.01 1
 
< 0.1%
0.33 1
 
< 0.1%
1.39 1
 
< 0.1%
1.64 1
 
< 0.1%
1.74 1
 
< 0.1%
2.15 1
 
< 0.1%
2.45 1
 
< 0.1%
2.71 1
 
< 0.1%
2.76 1
 
< 0.1%
ValueCountFrequency (%)
356179278.9 1
< 0.1%
356015889.4 1
< 0.1%
355553416.3 2
< 0.1%
355381433.6 1
< 0.1%
355380483.5 1
< 0.1%
355185537.1 1
< 0.1%
328431698.2 1
< 0.1%
328194464.9 1
< 0.1%
327998074.2 1
< 0.1%
327963024 1
< 0.1%

isFraud
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size48.5 MiB
0
6354407 
1
 
8213

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters6362620
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 6354407
99.9%
1 8213
 
0.1%

Length

2025-04-04T15:43:03.274594image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-04T15:43:03.360539image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 6354407
99.9%
1 8213
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 6354407
99.9%
1 8213
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6362620
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 6354407
99.9%
1 8213
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6362620
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 6354407
99.9%
1 8213
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6362620
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 6354407
99.9%
1 8213
 
0.1%

isFlaggedFraud
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size48.5 MiB
0
6362604 
1
 
16

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters6362620
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 6362604
> 99.9%
1 16
 
< 0.1%

Length

2025-04-04T15:43:03.467739image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-04T15:43:03.552997image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 6362604
> 99.9%
1 16
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 6362604
> 99.9%
1 16
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6362620
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 6362604
> 99.9%
1 16
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6362620
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 6362604
> 99.9%
1 16
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6362620
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 6362604
> 99.9%
1 16
 
< 0.1%

Interactions

2025-04-04T15:42:28.822831image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T15:41:16.238753image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T15:41:25.648927image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T15:41:35.752331image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T15:41:46.692782image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T15:41:56.962858image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T15:42:07.454748image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T15:42:18.434422image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T15:42:30.149438image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T15:41:17.428962image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T15:41:26.730520image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T15:41:37.114717image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T15:41:47.972835image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T15:41:58.232599image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T15:42:08.856596image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T15:42:19.712343image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T15:42:31.459779image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T15:41:18.627650image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T15:41:27.910924image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T15:41:38.436420image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T15:41:49.260453image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T15:41:59.502706image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T15:42:10.265824image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T15:42:20.998518image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T15:42:32.649950image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T15:41:19.821917image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T15:41:29.132192image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T15:41:39.854017image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T15:41:50.526600image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T15:42:00.779382image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T15:42:11.643177image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T15:42:22.313556image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T15:42:33.844018image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T15:41:20.970339image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T15:41:30.402043image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T15:41:41.210372image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T15:41:51.800507image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T15:42:02.027065image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T15:42:13.036576image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T15:42:23.618771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T15:42:35.036528image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T15:41:22.026590image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T15:41:31.832134image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T15:41:42.604228image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T15:41:53.082617image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T15:42:03.295010image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T15:42:14.370391image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T15:42:24.923347image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T15:42:36.221945image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T15:41:23.210106image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T15:41:33.085261image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T15:41:43.982476image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T15:41:54.372818image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T15:42:04.558961image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T15:42:15.743138image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T15:42:26.173464image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T15:42:37.364330image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T15:41:24.435421image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T15:41:34.359599image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T15:41:45.382741image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T15:41:55.652990image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T15:42:06.053008image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T15:42:17.133351image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T15:42:27.473692image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-04-04T15:43:03.644699image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
amountisFlaggedFraudisFraudnameDestnameOrignewbalanceDestnewbalanceOrigoldbalanceDestoldbalanceOrgsteptype
amount1.0000.0140.049-0.6070.0000.670-0.0710.5950.0480.0010.050
isFlaggedFraud0.0141.0000.0430.0010.0010.0000.0050.0000.0030.0060.005
isFraud0.0490.0431.0000.0250.0010.0020.0190.0020.0310.0590.059
nameDest-0.6070.0010.0251.000-0.000-0.643-0.037-0.606-0.0910.0040.480
nameOrig0.0000.0010.001-0.0001.0000.000-0.001-0.000-0.000-0.0000.000
newbalanceDest0.6700.0000.002-0.6430.0001.000-0.0940.936-0.008-0.0050.027
newbalanceOrig-0.0710.0050.019-0.037-0.001-0.0941.0000.0440.803-0.0110.238
oldbalanceDest0.5950.0000.002-0.606-0.0000.9360.0441.0000.024-0.0050.017
oldbalanceOrg0.0480.0030.031-0.091-0.000-0.0080.8030.0241.000-0.0060.213
step0.0010.0060.0590.004-0.000-0.005-0.011-0.005-0.0061.0000.011
type0.0500.0050.0590.4800.0000.0270.2380.0170.2130.0111.000

Missing values

2025-04-04T15:42:37.705161image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-04-04T15:42:42.872457image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

steptypeamountnameOrigoldbalanceOrgnewbalanceOrignameDestoldbalanceDestnewbalanceDestisFraudisFlaggedFraud
0139839.64757869170136.00160296.3616620940.00.0000
1131864.28218899821249.0019384.7217339240.00.0000
214181.001002156181.000.004396850.00.0010
311181.005828262181.000.0039169621182.00.0010
41311668.14344598141554.0029885.868289190.00.0000
5137817.71602652553860.0046042.2922472180.00.0000
6137107.771805947183195.00176087.2320633630.00.0000
7137861.642999171176087.23168225.5923140080.00.0000
8134024.368691402671.000.007689400.00.0000
9125337.77540727641720.0036382.2328296041898.040348.7900
steptypeamountnameOrigoldbalanceOrgnewbalanceOrignameDestoldbalanceDestnewbalanceDestisFraudisFlaggedFraud
6362610742463416.99562392863416.990.02404550.000.0010
6362611742163416.99633678363416.990.0195955276433.18339850.1710
636261274341258818.8217448531258818.820.01396040.000.0010
636261374311258818.8214321541258818.820.071274503464.501762283.3310
63626147434339682.133332123339682.130.02517080.000.0010
63626157431339682.135651847339682.130.05058630.00339682.1310
636261674346311409.2817372786311409.280.02609490.000.0010
636261774316311409.285339586311409.280.010822468488.846379898.1110
63626187434850002.522252932850002.520.03197130.000.0010
63626197431850002.52919229850002.520.05345956510099.117360101.6310